Faulty conditions in reactors employed in semiconductor manufacturing can lead to significant loss of revenue due to scrap and non-productive equipment downtime. In this regard, focus has been placed on equipment software that monitors operation of the equipment and creates alarms when unacceptable process excursions occur or other fault conditions are encountered.
However, what is needed is a method to determine the “health” of the equipment on an on-going basis so as to detect “emerging” fault conditions as well. Equipment and process engineers are asking the question—“How many more runs can I make before I need to do maintenance or repairs?” For example, take a batch furnace system used in semiconductor processing. For this equipment, some of the items the engineers are concerned about are: a) mass flow controller (MFC) drift; b) in-line mass flow meter (MFM) problems; c) leak rate; and d) heater elements.
In the past, both equipment manufacturers and chip manufacturers have relied on scheduled preventative maintenance (PM) of the equipment. However, this method is simply based on “rules of thumb” derived from average characteristics, such as mean time between failures (MTBF), and do not address detection, diagnosis, or prediction of faulty conditions for individual equipment.
Prior art includes several methods that attempt to detect equipment problems. One method described in Bottomfield, U.S. Pat. No. 6,195,621, relies on analyzing component vibration signature. However, this method is limited only to larger mechanical equipment that involves repetitive motion, such as pumps. This method does not address a majority of critical components, such as mass flow controllers, heaters, thermocouples, etc.
Another method described in Maekawa, U.S. Pat. No. 6,351,723, relies on extensions of previously known statistical process control (SPC) charts. However, this method focuses on single components and does not provide a system level detection or diagnosis. In addition, it does not address prediction at all.